1 Results

1.1 1-d Heatmap

1.2 Location meda_plots

1.3 Outliers as given by randomForest

1.4 Correlation Matrix

1.5 Cumulative Variance with Elbows

1.6 Paired Hex-binned plot

1.7 Hierarchical GMM Classifications

1.8 Hierarchical GMM Dendrogram

1.9 Stacked Means

1.10 Cluster Means

2 Restricting hGMM to \(K = 2\)

Here we are restricting hierarchical GMM to only go through on level. We are comparing the cluster results to the gaba labels.

set.seed(314)
h2 <- hmc(sdat, maxDepth = 2, ccol = ccol, maxDim = 12)
h2lab <- viridis(max(h2$dat$labels$col))

2.1 K = 2 stacked means plot

stackM(h2, ccol = ccol, centered = TRUE, depth = 1)

2.2 Pairs plot colored by true gaba classification

cols <- c("black", "magenta")[gabaID$gaba+1]
acols <- alpha(cols, 0.35)
pairs(h2$dat$data, pch = 19, cex = 0.4, col = acols)

2.3 Pairs plot colored by hGMM cluster classification

acols2 <- alpha(h2lab[h2$dat$labels$col], 0.5)
pairs(h2$dat$data, pch = 19, cex = 0.4, col = acols2)

3 Permutation test for ARI

p0 <- mclust::adjustedRandIndex(pred, gaba)
perms <- foreach(i = 1:1.5e4, .combine = c) %dopar% {
  set.seed(i*2)
  mclust::adjustedRandIndex(sample(pred), gaba)
}
hist(perms, xlim = c(min(perms), p0 + 0.25*p0),
     main = "permutation test of ARI values", probability = TRUE)
#hist(perms, probability = TRUE)
abline(v = p0, col = 'red')

4 Summary Table

measurment value
Misclassification Rate 0.1336696
Accuracy 0.8663304
Sensitivity 0.3960396
Specificity 0.9349711
Precision 0.4705882
Recall 0.3960396
F1-score 0.4301075
TP 40
FP 45
TN 647
FN 61